Instructions to use Deci/DeciLM-7B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Deci/DeciLM-7B-instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Deci/DeciLM-7B-instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Deci/DeciLM-7B-instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Deci/DeciLM-7B-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Deci/DeciLM-7B-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Deci/DeciLM-7B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Deci/DeciLM-7B-instruct

SGLang

How to use Deci/DeciLM-7B-instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Deci/DeciLM-7B-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Deci/DeciLM-7B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Deci/DeciLM-7B-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Deci/DeciLM-7B-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Deci/DeciLM-7B-instruct with Docker Model Runner:
```
docker model run hf.co/Deci/DeciLM-7B-instruct
```

danaevan commited on Feb 21, 2024

Commit

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@@ -9,7 +9,6 @@ datasets:
 DeciLM-7B-instruct is a model for short-form instruction following. It is built by LoRA fine-tuning on the [SlimOrca dataset](https://huggingface.co/datasets/Open-Orca/SlimOrca).
-### 🔥 Click [here](https://console.deci.ai/infery-llm-demo) for a live demo of DeciLM-7B + Infery!
 ## Model Details
@@ -127,7 +126,7 @@ Below are DeciLM-7B and DeciLM-7B-instruct's evaluation results.
 | Infery-LLM | A10 | 2048 | 2048 | **599** | 32 | 128 |
 - In order to replicate the results of the Hugging Face benchmarks, you can use this [code example](https://huggingface.co/Deci/DeciLM-7B/blob/main/benchmark_hf_model.py).
-- Infery-LLM, Deci's inference engine, features a suite of optimization algorithms, including selective quantization, optimized beam search, continuous batching, and custom CUDA kernels. To witness the full capabilities of Infery-LLM first-hand, we invite you to engage with our [interactive demo](https://console.deci.ai/infery-llm-demo).
 ## Ethical Considerations and Limitations

 DeciLM-7B-instruct is a model for short-form instruction following. It is built by LoRA fine-tuning on the [SlimOrca dataset](https://huggingface.co/datasets/Open-Orca/SlimOrca).
 ## Model Details
 | Infery-LLM | A10 | 2048 | 2048 | **599** | 32 | 128 |
 - In order to replicate the results of the Hugging Face benchmarks, you can use this [code example](https://huggingface.co/Deci/DeciLM-7B/blob/main/benchmark_hf_model.py).
+- Infery-LLM, Deci's inference engine, features a suite of optimization algorithms, including selective quantization, optimized beam search, continuous batching, and custom CUDA kernels. To explore the full capabilities of Infery-LLM, [schedule a live demo](https://deci.ai/infery-llm-book-a-demo/?utm_campaign=DeciLM%207B%20Launch&utm_source=HF&utm_medium=decilm7b-model-card&utm_term=infery-demo).
 ## Ethical Considerations and Limitations